class_docstr: Computes Precision, Recall and F1 with respect to a given `positive_label`.
  For example, for a BIO tagging scheme, you would pass the classification index of
  the tag you are interested in, resulting in the Precision, Recall and F1 score being
  calculated for this tag only.
docstr: # Returns
  
  precision : `float`
  recall : `float`
  f1-measure : `float`
docstr_tok: ["#","Returns","precision",":","`","float","`","recall",":","`","float","`","f1-measure",":","`","float","`"]
code: def get_metric(self, reset: bool = False) -> Dict[str, float]:
      """
      # Returns
  
      precision : `float`
      recall : `float`
      f1-measure : `float`
      """
      metric = super().get_metric(reset=reset)
      # Because we just care about the class `positive_label`
      # there is just one item in `precision`, `recall`, `fscore`
      precision = metric["precision"][0]
      recall = metric["recall"][0]
      f1 = metric["fscore"][0]
      return {"precision": precision, "recall": recall, "f1": f1}
  
file_path: allennlp/allennlp/training/metrics/f1_measure.py
imports: from typing import Dict
  from allennlp.training.metrics.metric import Metric
  from allennlp.training.metrics.fbeta_measure import FBetaMeasure
label_desc: Use image node_id 2 for calling the F1Measure obj's underlying member method code with example usage: obj.get_metric(reset) and returns: dict